Deep neural networks come in many sizes and architectures. The choice of architecture, in conjunction with the dataset and learning algorithm, affects the learned neural representations. Yet recent results have shown that different architectures learn representations with striking qualitative similarities. Vision transformers, for instance, align with human neural responses to natural images about as well as trained convolutional neural networks. Why might different systems learn similar represe…